One of the most difficult economic events for any county or city—indeed, household—is the sudden loss of employment. Rarely is it expected or welcome. Even if layoffs are not necessarily "surprising," people nonetheless hope against hope that it won't happen. But it often does.

In bad times, those job losses can pile up. For example, from 1990 to 1991, Stillwater County, Mont., and Miner County, S.D., lost about 3.5 percent of their employment. Dickinson County, Mich., and Koochiching County, Minn., suffered far worse fates, losing 9.7 percent and 17.1 percent of their jobs, respectively.

The good news for folks in Dickinson and Stillwater counties was that those counties rebounded with fairly strong employment growth throughout the remainder of the 1990s. By the end of the decade, employment in each county was well above what it had been prior to the decline. Koochiching and Miner counties were not so fortunate. Employment there never recovered to 1990 levels.

Some obvious questions come to mind. Why do counties experience employment shocks, and why do some counties (like Dickinson and Stillwater) rebound strongly while other counties (like Miner and Koochiching) struggle?

Economists point out, simply, that this is how market economies behave. Lots of jobs are gained and lost over the course of months and years as industries adjust to market forces and individual firms change their labor needs—sometimes higher, sometimes lower—to help them stay competitive (see "Helicopter churn: The macro view on job loss and growth" for additional discussion on job churn).

But that's a cold salve for communities suffering a major job loss. Indeed, the supposed efficiency of market economies often masks geographic unevenness of job growth and loss. Losing a manufacturing plant does not conveniently ensure a new telecom firm in town. That potential imbalance creates a lot of local angst.

Given the anxiety attached to job loss—for workers, their households and their communities—the fedgazette decided to take a closer look at county employment "shocks" (defined as an employment decline of 3 percent or more) during the brief period from 1990 to 1993 (see "Methodology: A County slice in time" for more on methodology).

During this span of four short years, 40 of the district's 303 counties experienced annual employment losses of at least 3 percent (some more than once; there were a total of 48 county shock "events" during this period). The intent was to search for commonalities and differences among these counties, both before and after the employment shock, that might help us better understand these events—why they happen and what subsequent effect they have on a county's growth trajectory.

In a nutshell, counties that experienced an employment shock from 1990 to 1993 follow no clear, consistent pattern. Counties from all six states in the district were struck. Counties were more likely to be small and farm-dependent—but being small and farm-dependent didn't automatically mean a county was in for it. More interesting—though perhaps unsettling—is the wide diversity in the types of counties that were "shocked" and the lack of predictability in how well counties would rebound from their employment shock.

This article sticks mostly to a macro or "helicopter" view of employment shocks by focusing on countywide data. It ignores the many unique circumstances and economic stories that surround the shock and subsequent employment recovery in each county and its many resident communities. Other articles in this fedgazette pursue a more on-the-ground "bus" approach in an effort to fill in that anecdotal void and provide richer insight to the nature and effects of employment decline.

Now, into the helicopter.

Pink slippage

To anyone familiar with rural issues, it will come as no surprise that small counties with relatively low employment and a heavy reliance on farming were more likely to experience a large percentage decline in employment.

Having low employment was clearly associated with being "shocked." The median 1989 employment of shocked counties was 1,534, compared to 5,376 for nonshocked counties. Furthermore, 12 of the 18 district counties with employment of 1,000 or below suffered an employment shock. In some cases, the 3 percent "shock threshold" means only a small number of jobs were lost. Buffalo County, S.D., for example, lost just 15 (net) jobs in 1990, but that's from a job base of 488.

The largest employment counties were immune from shocks. From 1990 to 1993, none of the 40 largest employment counties in the district, including the 29 officially designated as metropolitan statistical areas (MSAs), suffered a 3 percent annual employment decline (despite sizable job losses in some of those counties in terms of numbers, rather than percentage). But not all shocked counties were small. Freeborn County, Minn., and Dickinson County, Mich., both had 1989 employment of better than 15,000.

Given the importance of employment size, it is hardly surprising that counties with a large percentage of workers in the farm sector were more likely to suffer an employment shock. The median shocked county had a farm share of employment of 25.9 percent, compared to 14.5 percent for nonshocked counties. Furthermore, it is well known that employment growth in many rural communities has been slow or negative for the past few decades, with people and employees steadily migrating to metropolitan areas and other regional centers. (For further discussion on rural growth trends during the 1990s, read an analysis in the September 2002 fedgazette.)

In a way, there's not much news so far: Rural farming communities were more likely to experience an employment shock in the early 1990s. But that statement masks huge differences in the shocked counties. It has already been noted that some shocked counties had relatively large populations. Furthermore, one-quarter of the shocked counties had farm employment shares of 11 percent or lower, well below the median of counties that experienced no employment shock. Dickinson and Koochiching are relatively large and had very little farm employment.

To dig further beneath the surface, the fedgazette investigated other possible tendencies and relationships for further insights regarding the sources and effects of employment shocks (again, see methodology sidebar). This additional analysis reveals that shocked counties have surprisingly little in common along other socioeconomic and demographic dimensions, including income per capita, education levels, age and industry mix, and location next to a metropolitan area (see table for a comparison of statistics between shocked and nonshocked counties).

For example, while shocked counties had slightly lower income per capita than nonshocked counties, the difference is small relative to the large disparities across counties. Billings County, N.D., and Waseca County, Minn., were shocked counties with 1989 income per capita of roughly $11,500, well above the median income in nonshocked counties. Incomes in Dickinson and Rosebud counties were even higher. While some low-income counties did experience employment shocks, many did not. The lowest-income county, Keweenaw County, Mich. (1989 income per capita of $3,036), did not experience a shock and grew robustly in terms of both employment and income per capita through the 1990s.

Also surprising is that educational attainment was roughly similar in shocked and unshocked counties. The Montana counties of Jefferson and Judith Basin learned that having a relatively high fraction of the population with a college degree (roughly 20 percent each) did not immunize them from being shocked. However, none of the 14 counties with a college degree percentage of 25 percent or higher suffered a shock, though it should be noted that most of these counties were comparatively high-employment counties, many located in the metropolitan areas.

Similarly, the industry mix and age composition in shocked counties was not substantially different than in nonshocked counties, except for the farm share. Manufacturing and service shares were somewhat lower in shocked counties, but again the disparity across counties was large. The percentages of young and old people were similar in shocked and nonshocked counties. The percentage of foreign-born residents and the poverty rate also showed little consistent pattern.

Finally, since no metropolitan area suffered a shock, and since metro areas generally have seen strong growth since 1990, one might think that being near a metropolitan area would make a county less likely to be shocked. In data terms, that's true, but barely: About 13 percent of counties that border a metropolitan county suffered a shock, while 16 percent of nonbordering counties did so.

Even the recovery trends for counties bordering an MSA are inconclusive. For example, four of the eight best employment recoveries among shocked counties were experienced in counties bordering an MSA. However, median employment growth was higher among counties that did not border an MSA versus those that did.

Who recovered?

So, except for employment size, there isn't a particularly strong pattern among the types of counties that experienced an employment shock between 1990 and 1993. But what about their subsequent recoveries? First, which counties exhibited strong or weak recoveries after experiencing an employment shock? Second, is there any predictability to counties' post-shock employment performance, whether good or bad?

The fedgazette looked at employment recovery in each of the 40 shocked counties from the year of a county's shock through (a smoothed) 2001. Maybe not surprisingly, employment in the shocked counties grew at an average rate of 1.0 percent per year, while employment in the other 263 district counties grew more briskly at 1.3 percent annually.

That difference is notable, particularly given that county growth rates don't include job losses absorbed during the shock itself, thus giving shocked counties something of an artificially low starting point. But again, these averages mask large differences among the shocked counties, ranging from 5.5 percent in Stillwater County, Mont., to -0.9 percent in Miner County, S.D.

To compare the characteristics of shocked counties with strong and weak employment recoveries, these counties were split into three categories: strong, weak and remainder. Strong includes the 14 counties with annual employment growth of 1.5 percent or higher. Weak comprises the 15 counties with 0.3 percent employment growth or less. The 11 remainder counties fall in the middle. (See table for characteristics of strong- and weak-recovery counties. See district map for illustration of strong and weak counties.)

What is striking in the comparison of counties with strong and weak recoveries is how similar they are from our bird's-eye view. While strong-recovery counties had slightly higher incomes and education levels on average, the variability within each group is much larger than the variability across the two groups. For example, Buffalo County, S.D., and Jefferson County, Mont., both had very strong employment recoveries, but Buffalo had a very low education level (4.2 percent with college degree) while Jefferson had a very high education level (20.8 percent with college degree).

This same argument holds for the age and industry composition, with one exception. Counties with weak recoveries did tend to have higher farm shares than the counties with strong recoveries.

The situation looks different across all district counties. Counties (shocked or not) that had higher employment levels, higher educational attainment, a larger share of working-age population (16 to 65 years old) and higher employment growth in the 1980s tended to have higher employment growth during the mid to late 1990s. These characteristics are much less distinctive among the three groups of shocked counties. On the negative side, characteristics associated with slower employment growth (across all counties) are higher farm employment shares and higher percentage of the population over age 65.

Beyond employment

It's natural for the public to obsess over jobs. Having a job is crucial for most working-age people, and counties typically seek to maintain and build employment opportunities.

But employment growth alone does not provide a complete picture of the economic performance of counties. A host of other ingredients might be considered—ingredients that do not move in tandem with employment. As such, characterizing counties as "successful" or not based solely on their employment recoveries might be premature, even a bit misleading.

Consider earned income per capita. Golden Valley County, Mont., had a robust 3.0 percent annual employment growth from 1991 to 2001—more than two times the average for all district counties, shocked or not. But income per capita fell by 2.5 percent per year. In contrast, Towner County, N.D., had the second-lowest annual employment growth of all 40 shocked counties at -0.4 percent, but had a robust growth in income of 5.0 percent per year.

Shocked counties in our analysis did, on average, have lower employment and lower income growth than nonshocked counties during the recovery period. But shocked counties with strong employment recoveries actually had roughly the same income growth (3.4 percent) as shocked counties with weak employment recoveries (3.2 percent).

Poverty rates fell by 2.9 percentage points on average across the district from 1989 to 1999. Not surprising, shocked counties on average had a smaller decline in poverty rates (1.3 percentage points) than nonshocked counties (3 percentage points). More surprising is that shocked counties with strong employment recoveries saw a smaller decline in poverty rates (0.9 percentage points) than shocked counties with weak recoveries (1.1 percentage points).

Economic theory suggests several reasons employment, income and poverty rates may not move as expected. For example, competitive pressures in the mining industry have led to the loss of many jobs on northern Minnesota's Iron Range, but increasing productivity has allowed wages to increase for those that remain. The same phenomenon is likely true in farm-dependent counties as farm employment declines but average farm size gets bigger. But delving into these issues is beyond the scope of this article. The point is that there is more to consider in analyzing county economies than employment alone.

Indeed, there is more to be learned by getting a closer, on-the-ground inspection of some of these individual counties. Other stories in this fedgazette provide this up-close perspective. Enjoy the bus ride.

Terry Fitzgerald is an adviser to the Federal Reserve Bank of Minneapolis and economics professor at St. Olaf University. Elizabeth Ball is a recent graduate of St. Olaf, and Mark Holland is a senior there.